Can Femtech deliver radically personalized care to women?

Patient privacy and safety have always been cornerstones of the U.S. healthcare system. But in today’s digital era, there are apps tracking the most sensitive information such as the female menstrual cycle and fertility window. The collection of this data might be valuable for the future of healthcare – the issue really comes down to data control vs. data privacy.

It’s hard to reconcile data and technology in healthcare, where nothing is more personal and nuanced than one’s health. Yet, over the past three to four years, we’ve seen the healthcare industry apply technology to unpack the value drivers of health personalization by organizing and actioning customer and clinical data. The reasoning is simple – technology allows us to take data from a human scale to a digital scale.

To deliver meaningful care, you have to know your patient – who they are, where they are, and what they’re looking for. You have to be able to deliver relevant experiences to them that drive real benefit because what works for one woman doesn’t necessarily work for another. You need data to do this. There’s an undeniable need for education like the articles and resources found in many femtech apps and gadgets geared at enhancing a women’s wellbeing, mostly focusing on fertility, reproductive health, or menstrual health. But there’s a disconnect between the information a user is asked to input and the tools and information returned, like calendar alerts. Unlike biometric data collected by fitness apps or Apple Watch, the level of data input required for many femtech apps can often feel never ending and incredibly personal – data from the date, time and flow of your menstrual cycle, to intimate details of sexual history and even information most rarely think of like “cervical mucus quality”.

The Role of Machine Learning in Femtech

The beauty of machine learning is that technology iteratively improves it’s understanding and analysis of data over time as more data points are captured and analyzed. The reverse is also true; without adequate updating and ongoing maintenance, the usability of data actually decays over time. Even subtle changes in data, as in life, can have a major impact on the outcomes and because there are a large number of variables in women’s health, the ability for data models to accurately reflect what is happening in the real world can be limited. Revolutionary advances in personalized healthcare will occur when we integrate data from multiple sources – including apps, wearables, and medical devices to remotely collect data as a resource for medical advancements.

The potentials of digital health and the need to protect private information are not fundamentally opposed, but they do need to be balanced. We need to get better about transparency of use and empowering the individual’s rights over their data. Data is a valuable commodity that should be used for good in an open and transparent way to deliver better care. Individuals should have the ability to permission their data, share it, donate it or sell it as they see fit. The issue is really about data control versus data privacy and giving individuals the ability to opt out of certain messaging. We have seen from the frontline the impacts of GDPR and CCPA, and how they empower consumers to have the option to provide permission on what information they choose to share and have utilized if they’d prefer to receive more personalized care or messaging – especially for women who want more control over their health and lives.

Having access to healthcare data for the purpose of general research to find new drug targets and better select patients for clinical studies shouldn’t be overlooked. Femtech has the opportunity to present a smarter way for pharma to find qualified patients for existing trials and even pave the way for discovering whole cohorts of patients before a trial is even designed. This will enable consumers to participate in clinical research on their terms, and at the same time, begin to resolve the biggest pain point in drug development today: patient recruitment for trials.

Women’s health goes beyond family planning and fertility. Certain diseases like autoimmune diseases have a 3x higher prevalence in women than men and should be addressed. All women are not all the same and in healthcare, ignoring the differences can risk lives. To go beyond the surface level, femtech needs to also understand social determinants of health to reduce human biases, improve outcomes and our healthcare system.

Conclusion

Female health is complex and emotionally charged. It encompasses a wide range of physical factors and health conditions. It’s nuanced and requires technology that can understand those distinctions to really provide value to women. It is important to understand the underlying variables contributing to differences between health outcomes in women and men – because there are real biological differences at the molecular and cellular level that may contribute to differences in outcomes. Applying data and technology solutions can do this and uncover new forms of diagnostics and treatments for the future of health.